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Record W2902041985 · doi:10.5539/eer.v8n2p55

Energy Conservation Behavior of Thai University Students

2018· article· en· W2902041985 on OpenAlexvenueno aff
Kunanunt Thayayuth, Paitoon Pimdee

Bibliographic record

VenueEnergy and Environment Research · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsEnergy conservationSample (material)Context (archaeology)SustainabilityPopulationPsychologyEnergy (signal processing)GeographySociologyDemographyPhysicsMathematicsStatisticsEcologyBiology

Abstract

fetched live from OpenAlex

An estimated 1.2 billion people, or 16% of the global population, did not have access to electricity in 2015. Therefore, access and the conservation of energy have become critical issues in a country’s quest for economic prowess and sustainability. This research, therefore, aimed to study the energy-conservation behavior of university students, and compare their energy-saving behavior categorized by gender and university group. The sample of 900 undergraduate students came from 15 Thai public universities under the Office of the Higher Education Commission [OHEC] in the 2013 academic year. The sample was randomly selected using a multi-stage sampling method. The instrument used to collect data in this research was a 5-level rating-scale questionnaire with reliability which was between 0.86-0.94. Data were analyzed using mean, standard deviation, t-test for independent sample and one-way ANOVA. The findings revealed that the students exhibited energy-conservation behavior in a family context at a high level, while energy-conservation behavior for themselves, and for the public was at a moderate level. Male and female students had different energy-conservation behaviors, and students under different university groups had distinct energy-conservation behaviors.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.244
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.026
GPT teacher head0.307
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2018
Admission routes1
Has abstractyes

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